<html lang="en"> <head> <title>Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission</title> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <link href="https://unpkg.com/@stencila/thema@2/dist/themes/elife/styles.css" rel="stylesheet"> <script src="https://unpkg.com/@stencila/thema@2/dist/themes/elife/index.js" type="text/javascript"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.esm.js" type="module"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.js" type="text/javascript" nomodule=""></script> </head> <body> <main role="main"> <article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root"> <h1 itemprop="headline" content="Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications f…"> Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission</h1> <meta itemprop="image" content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Both%20consumptive%20and%20non-consumptive%20effects%20of%20predators%20impact%20mosquito%20populations%20and%20have%20implications%20f%E2%80%A6"> <ol data-itemprop="authors"> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Marie C Russell"><span data-itemprop="givenNames"><span itemprop="givenName">Marie</span><span itemprop="givenName">C</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Russell</span></span><span data-itemprop="emails"><a itemprop="email" href="mailto:marie.clare.russell@gmail.com">marie.clare.russell@gmail.com</a></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Catherine M Herzog"><span data-itemprop="givenNames"><span itemprop="givenName">Catherine</span><span itemprop="givenName">M</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Herzog</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-2">2</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Zachary Gajewski"><span data-itemprop="givenNames"><span itemprop="givenName">Zachary</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Gajewski</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Chloe Ramsay"><span data-itemprop="givenNames"><span itemprop="givenName">Chloe</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Ramsay</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-4">4</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Fadoua El Moustaid"><span data-itemprop="givenNames"><span itemprop="givenName">Fadoua</span></span><span data-itemprop="familyNames"><span itemprop="familyName">El</span><span itemprop="familyName">Moustaid</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Michelle V Evans"><span data-itemprop="givenNames"><span itemprop="givenName">Michelle</span><span itemprop="givenName">V</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Evans</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a><a itemprop="affiliation" href="#author-organization-6">6</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Trishna Desai"><span data-itemprop="givenNames"><span itemprop="givenName">Trishna</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Desai</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-7">7</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Nicole L Gottdenker"><span data-itemprop="givenNames"><span itemprop="givenName">Nicole</span><span itemprop="givenName">L</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Gottdenker</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a><a itemprop="affiliation" href="#author-organization-8">8</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Sara L Hermann"><span data-itemprop="givenNames"><span itemprop="givenName">Sara</span><span itemprop="givenName">L</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Hermann</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-9">9</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Alison G Power"><span data-itemprop="givenNames"><span itemprop="givenName">Alison</span><span itemprop="givenName">G</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Power</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-10">10</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Andrew C McCall"><span data-itemprop="givenNames"><span itemprop="givenName">Andrew</span><span itemprop="givenName">C</span></span><span data-itemprop="familyNames"><span itemprop="familyName">McCall</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-11">11</a></span> </li> </ol> <ol data-itemprop="affiliations"> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1" id="author-organization-1"><span itemprop="name">Department of Life Sciences, Imperial College London, Silwood Park Campus</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Ascot</span><span itemprop="addressCountry">United Kingdom</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2" id="author-organization-2"><span itemprop="name">Center for Infectious Disease Dynamics, Pennsylvania State University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">University Park</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-3" id="author-organization-3"><span itemprop="name">Department of Biological Sciences, Virginia Polytechnic Institute and State University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Blacksburg</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-4" id="author-organization-4"><span itemprop="name">Department of Biological Sciences, University of Notre Dame</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Notre Dame</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-5" id="author-organization-5"><span itemprop="name">Odum School of Ecology & Center for Ecology of Infectious Diseases, University of Georgia</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Athens</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-6" id="author-organization-6"><span itemprop="name">MIVEGEC, IRD, CNRS, Université Montpellier</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Montpellier</span><span itemprop="addressCountry">France</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-7" id="author-organization-7"><span itemprop="name">Nuffield Department of Population Health, University of Oxford</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Oxford</span><span itemprop="addressCountry">United Kingdom</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-8" id="author-organization-8"><span itemprop="name">Department of Veterinary Pathology, University of Georgia College of Veterinary Medicine</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Athens</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-9" id="author-organization-9"><span itemprop="name">Department of Entomology, Pennsylvania State University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">University Park</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-10" id="author-organization-10"><span itemprop="name">Department of Ecology & Evolutionary Biology, Cornell University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Ithaca</span><span itemprop="addressCountry">United States</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-11" id="author-organization-11"><span itemprop="name">Biology Department, Denison University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Granville</span><span itemprop="addressCountry">United States</span></address></li> </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher"> <meta itemprop="name" content="Unknown"><span itemscope="" itemtype="http://schema.org/ImageObject" itemprop="logo"> <meta itemprop="url" content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown"> </span> </span><time itemprop="datePublished" datetime="2022-01-19">2022-01-19</time> <ul data-itemprop="genre"> <li itemprop="genre">Research Article</li> </ul> <ul data-itemprop="about"> <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span itemprop="name">Ecology</span></li> <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span itemprop="name">Epidemiology and Global Health</span></li> </ul> <ul data-itemprop="keywords"> <li itemprop="keywords">vector ecology</li> <li itemprop="keywords">predation</li> <li itemprop="keywords">disease dynamics</li> <li itemprop="keywords">meta-analysis</li> <li itemprop="keywords">Mosquito</li> </ul> <ul data-itemprop="identifiers"> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/publisher-id"><span itemprop="name">publisher-id</span><span itemprop="value" data-itemtype="http://schema.org/Number">71503</span> </li> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi"> <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.71503</span> </li> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/elocation-id"><span itemprop="name">elocation-id</span><span itemprop="value">e71503</span> </li> </ul> <section data-itemprop="description"> <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2> <meta itemprop="description" content="Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes , Anopheles , and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission."> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em>, and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.</p> </section> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction </h2> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While it is well known that predation reduces vector populations through consumptive effects, non-consumptive effects of predators can also greatly impact prey demographics <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib106"><span>106</span><span>Preisser et al.</span><span>2004,12,31</span></a></cite>. Mosquitoes are vectors of a variety of debilitating and deadly diseases, including malaria, lymphatic filariasis, and arboviruses, such as chikungunya, Zika, and dengue <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib147"><span>147</span><span>Weaver and Reisen</span><span>2009,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib148"><span>148</span><span>WORLD HEALTH ORGANIZATION</span><span>2019,12,31</span></a></cite></span>. Consequently, there is motivation from a public health perspective to better understand the different drivers of variation in mosquito traits that can ultimately impact vector population growth and disease transmission. In addition, recent work has suggested that incorporation of vector trait variation into disease models can improve the reliability of their predictions <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Cator et al.</span><span>2019,12,31</span></a></cite>. In this study, systematic review and meta-analysis methods are used to synthesize a clearer understanding of the consumptive and non-consumptive effects of predators on mosquito traits, including survival, oviposition, development, and size.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Mosquito insecticide resistance is recognized as a growing problem <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib54"><span>54</span><span>Hancock et al.</span><span>2017,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib57"><span>57</span><span>Hemingway and Ranson</span><span>1999,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib82"><span>82</span><span>Liu</span><span>2014,12,31</span></a></cite></span> leading some to suggest that control efforts should rely more heavily on ‘non-insecticide based strategies’ <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Benelli et al.</span><span>2015,12,31</span></a></cite>. The consumptive effects of predators on mosquitoes have previously been harnessed for biocontrol purposes. Past biocontrol efforts have used predators such as cyclopoid copepods <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib69"><span>69</span><span>Kay et al.</span><span>2001,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Marten</span><span>1989,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib120"><span>120</span><span>Russell et al.</span><span>1995,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib142"><span>142</span><span>Veronesi et al.</span><span>2014,12,31</span></a></cite></span> and mosquitofish <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib107"><span>107</span><span>Pyke</span><span>2007,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib121"><span>121</span><span>Seale</span><span>1916,12,31</span></a></cite></span> to target the mosquito’s aquatic larval stage. The strength of the consumptive effects of these predators on mosquitoes can be influenced by multiple factors, including predator-prey size ratio and temperature. Predator-prey body size ratios tend to be higher in freshwater habitats than other types of habitats <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Brose et al.</span><span>2005,12,31</span></a></cite>, and attack rate tends to increase with temperature <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib68"><span>68</span><span>Kalinoski and DeLong</span><span>2015,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib29"><span>29</span><span>Dam and Peterson</span><span>1987,12,31</span></a></cite></span>, although other studies suggest a unimodal response to temperature <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib139"><span>139</span><span>Uiterwaal and Delong</span><span>2019,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib41"><span>41</span><span>Englund et al.</span><span>2010,12,31</span></a></cite></span>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Predators can also have non-consumptive effects on prey <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib103"><span>103</span><span>Peacor and Werner</span><span>2000,12,31</span></a></cite>, and these effects are thought to be more pronounced in aquatic ecosystems than in terrestrial ecosystems <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib106"><span>106</span><span>Preisser et al.</span><span>2004,12,31</span></a></cite>. Non-consumptive effects of predators are the result of the prey initiating anti-predator behavioral and/or physiological trait changes that can aid in predator avoidance <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib59"><span>59</span><span>Hermann and Landis</span><span>2016,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib80"><span>80</span><span>Lima and Dill</span><span>1989,12,31</span></a></cite></span>. Such plasticity in certain prey traits may also result in energetic costs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib81"><span>81</span><span>Lima</span><span>1997,12,31</span></a></cite>. Predator detection is key for these trait changes to occur and can be mediated by chemical, tactile, and visual cues <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib58"><span>58</span><span>Hermann and Thaler</span><span>2013,12,31</span></a></cite>. In mosquitoes, exposure to predators is known to affect a variety of traits including behavior, size, development, and survival <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Arav and Blaustein</span><span>2005,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib17"><span>17</span><span>Bond et al.</span><span>2004,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib114"><span>114</span><span>Roberts</span><span>2011,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib119"><span>119</span><span>Roux et al.</span><span>2014,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib151"><span>151</span><span>Zuharah et al.</span><span>2012,12,31</span></a></cite></span>. Experimental observations of predator effects on mosquito size and development are inconsistent and results sometimes vary by mosquito sex. For example, exposure to predation was found to increase the size of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex pipiens</em> mosquitoes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Alcalay et al.</span><span>2017,12,31</span></a></cite> but decrease the size of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta longiareolata</em><span data-itemtype="http://schema.stenci.la/Null">null</span><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib128"><span>128</span><span>Stav et al.</span><span>2004,12,31</span></a></cite>. In addition, female <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes triseriatus</em> exhibited shorter development times when exposed to predation at high nutrient availability <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib100"><span>100</span><span>Ower and Juliano</span><span>2018,12,31</span></a></cite>, but male <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">C. longiareolata</em> had longer development times in the presence of predators <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib128"><span>128</span><span>Stav et al.</span><span>2004,12,31</span></a></cite>. In some cases, a shared evolutionary history between predator and prey organisms can strengthen the non-consumptive effects of predators on mosquitoes <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Buchanan et al.</span><span>2016,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib124"><span>124</span><span>Sih</span><span>1985,12,31</span></a></cite></span>. </p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This investigation assesses the consumptive and non-consumptive effects of predators on mosquito traits and describes how these effects could impact disease transmission. The roles of vector genus, predator family, mosquito larval instar (an indicator of prey size), and temperature are also examined as potential moderators of predator effects. Non-consumptive effects of predators are expected to cause a smaller reduction in mosquito survival than consumptive effects because, in practice, measures of consumptive effects always include both consumptive and non-consumptive effects. Based on previous findings, larger predators are more likely to consumptively reduce mosquito survival <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib74"><span>74</span><span>Kumar et al.</span><span>2007,12,31</span></a></cite>. In addition, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> mosquito larvae may be more vulnerable to consumption than other genera because of the high degree of motility observed in this genus <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib35"><span>35</span><span>Dieng et al.</span><span>2002,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib89"><span>89</span><span>Marten and Reid</span><span>2006,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib126"><span>126</span><span>Soumare and Cilek</span><span>2010,12,31</span></a></cite></span>. The oviposition response to predation is expected to be weakest among <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> species that oviposit above the water line, due in part to their delayed-hatching eggs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>. Predation is predicted to reduce mosquito size and lengthen development time, consistent with the reduced growth response observed in other insect systems <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib59"><span>59</span><span>Hermann and Landis</span><span>2016,12,31</span></a></cite>. Certain non-consumptive effects of predation, particularly oviposition site selection and decreased vector size, are likely to play important roles in the dynamics of mosquito-borne disease.</p> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods"> Materials and methods</h2> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="literature-screening"> Literature screening</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A systematic search was conducted for studies on predation of mosquitoes that were published between 1970 and July 1, 2019 using both PubMed and Web of Science search engines, according to the PRISMA protocol <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib93"><span>93</span><span>Moher et al.</span><span>2008,12,31</span></a></cite>. Mosquito vectors of the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> genera were specifically highlighted in our search terms because these genera contain the vector species that transmit malaria, yellow fever, and dengue – the three most deadly mosquito-borne diseases worldwide <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib62"><span>62</span><span>Hill et al.</span><span>2004,12,31</span></a></cite>. Searches included 18 combinations of three vector predation terms (mosquito predat*, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em> predat*, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> predat*) and six trait terms (survival, mortality, development, fecundity, dispers*, host preference). Abstracts from the 1136 studies were each screened by two different co-authors, using the ‘metagear’ package in R <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib76"><span>76</span><span>Lajeunesse</span><span>2015,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib108"><span>108</span><span>R Development Core Team</span><span>2019,12,31</span></a></cite></span>. If either screener thought the study had information relevant to predation of mosquitoes, or both screeners thought the abstract was ambiguous, the study was read in full. This resulted in 306 studies that were fully reviewed to determine if any predation data could be extracted (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>).</p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1" title="Figure 1."> <label data-itemprop="label">Figure 1.</label><img src="index.html.media/fig1.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="flowchart-demonstrating-the-literature-search-screening-process-data-exclusions-and-the-resulting-seven-different-vector-trait-data-subsets"> Flowchart demonstrating the literature search, screening process, data exclusions, and the resulting seven different vector trait data subsets.</h4> </figcaption> </figure> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="study-exclusion-criteria"> Study exclusion criteria</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data were extracted from studies that collected data on non-consumptive and/or consumptive effects of predators on mosquitoes. Studies were required to have a mean, error measurement, and at least two replicates for both control and predator treatments. The control treatment was required to have all the same conditions as the predator treatment, such as prey density and type of water, without the predators. Studies that were not published in English and studies that did not differentiate between predators of multiple families were excluded. Studies were also excluded if oviposition by free-flying female mosquitoes could have interfered with observing the consumptive effects of predators on vector survival. The final database comprised data extracted from 60 studies (<a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>). The data included observations from laboratory experiments, as well as semi-field experiments, in which mesocosms of different treatments were observed in outdoor settings.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-extraction">Data extraction</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Variables related to the publication, the vector, the predator, and the effect size (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>) were extracted from each study. Data from tables and text were recorded as they were published, and data from figures were extracted using WebPlotDigitizer <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib116"><span>116</span><span>Rohatgi</span><span>2019,12,31</span></a></cite>. Error measurements that were not originally presented as standard deviations were converted to standard deviations prior to the effect size calculation.</p> <table id="table1" itemscope="" itemtype="http://schema.org/Table"> <caption><label data-itemprop="label">Table 1.</label> <div itemprop="caption"> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="variables-extracted-from-included-studies">Variables extracted from included studies.</h4> </div> </caption> <thead> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Variable</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Description</th> </tr> </thead> <tbody> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Publication data:</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Title</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Full study title</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Journal</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Name of journal that published the study</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Year</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Year of publication</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Study environment</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Environment where the experiment took place: lab or semi-field</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vector data:</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Order, Family, Genus, Species</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Taxonomic identification </td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Trait</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Outcome that was measured (e.g. survival, development, etc.)</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Stage</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Life stage: egg, larva, pupa, or adult</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Larval instar</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Early (1st and 2nd instars), late (3rd and 4th instars), both, or NA (eggs, pupae, or adults)</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sex</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Male or female</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator data:</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phylum, Class, Order, Family, Genus, Species</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Taxonomic identification </td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Starved</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Whether the predator was starved: yes or no</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Time starved</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Amount of time that the predator was starved (in minutes)</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predation effect</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Consumptive or non-consumptive</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Effect size data:</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Units</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Units of extracted data </td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Control mean</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Average of the outcome measured among the controls</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Control standard deviation</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Standard deviation of the outcome measured in the controls</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Control number of replicates</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of control replicates</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predation mean</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Average of the outcome measured in the predator treatment</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predation standard deviation</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Standard deviation of the outcome measured in the predator treatment</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predation number of replicates</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of predation replicates</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Experiment ID</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Alphabetic assignment to mark observations sharing a control group or representing the same prey individuals as originating from the same experiment</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Additional data:</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Experiment time (days) </td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Duration of the experiment in days</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Data source</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Graph or text</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of predators</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of predators with access to prey, or ‘cue’ if there are no predators with direct access to prey</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of prey (vectors) </td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Number of mosquito prey that are exposed to predation</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Arena volume (mL)</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Volume of the arena where prey encounter predators</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Time exposed to predator(s)</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Amount of time (in days) when the predator has direct access to the mosquito prey</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Temperature (°C)</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Temperature during the predation interaction</td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Type of predator cue </td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator cues, or cues from both predator(s) and dying conspecifics; NA for observations with a consumptive predation effect</td> </tr> </tbody> </table> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-exclusions">Data exclusions</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A PRISMA plot of literature inclusion and exclusion is provided in <a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>. Observations where insecticide was used were excluded because insecticides are known to interfere with consumptive and non-consumptive effects of predators <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib32"><span>32</span><span>Delnat et al.</span><span>2018,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib63"><span>63</span><span>Janssens and Stoks</span><span>2011,12,31</span></a></cite></span>. In addition, observations from experiments with mosquito prey of two or more species were excluded because it was not possible to account for effects from apparent competition or prey-switching. Observations of vector fecundity, vector competence, behavioral traits other than oviposition, as well as observations where the vector trait was marked as ‘other’ were not analyzed because each of these traits were only recorded from three or fewer studies.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Due to protandry, the earlier emergence of males to maximize their reproductive success, mosquitoes respond to sex-specific selective forces that influence their development time and body size <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib71"><span>71</span><span>Kleckner et al.</span><span>1994,12,31</span></a></cite>. Under low resource conditions, female mosquitoes are likely to maximize body mass by extending their development time, whereas males tend to minimize their development time at the expense of lower body mass <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib71"><span>71</span><span>Kleckner et al.</span><span>1994,12,31</span></a></cite>. Observations of mosquito development time and body size in our database that were not sex-specific were excluded so that these vector traits could be analyzed while controlling for sex. In addition, among the observations of development time and body size, some predator means did not necessarily represent an evenly weighted average of the replicates. For example, if a total of 20 mosquitoes from three different predator replicates survived to adulthood, the mean development time and size of those 20 individuals may have been reported. To represent an evenly weighted average of the replicates, it is necessary to first calculate summary statistics among multiple individuals that emerge from the same replicate, and then report the average of the replicate-specific means. Observations that might have been influenced by uneven representation of replicates were excluded to prevent pseudo-replication from altering later meta-analyses.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For consumptive observations where life stage-specific survival was reported after more than 10 days of predator exposure, only data on survival marked by adult emergence were included for analysis. Effects observed among immature vector stages after such a long period of predator exposure were not analyzed because they could have resulted from a combination of non-consumptive effects on development, and consumptive effects on survival. Development time observations that were reported as the inverse of development time (units of days<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">–1</sup>) were excluded because although their means could be converted to units of days, their standard deviations could not be converted to match units of days. In cases where multiple body sections of the same mosquitoes were measured to produce multiple size observations, only the wing measurement was included in the analysis to prevent pseudo-replication. Observations in which both the control and the predator treatments had standard deviations of zero were excluded because the meta-analysis methods did not support non-positive sampling variances.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="exclusions-and-data-substitutions-for-predator-treatment-means-of-zero">Exclusions and data substitutions for predator treatment means of zero</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">One study that was included in our database reported egg survival data as the hatch rate of field collected <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex pervigilans</em> rafts <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib151"><span>151</span><span>Zuharah et al.</span><span>2012,12,31</span></a></cite>. However, mosquitoes have been shown to lay eggs independent of mating <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib101"><span>101</span><span>O’Meara</span><span>1978,12,31</span></a></cite>, and hatch rates of zero have previously been observed in rafts laid by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> females that were held separately from males <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib131"><span>131</span><span>Su and Mulla</span><span>1996,12,31</span></a></cite>. Thus, hatch rates of zero were excluded from further analysis because these values may represent unfertilized egg rafts, rather than a strong impact of predators on survival. Twenty of the 187 consumptive survival observations had a predation mean of zero, and each of these zeros resulted from experiments that began with a specified number of live larvae. Consumptive survival zeros were each replaced with 0.5% of the starting number of mosquito prey to avoid undefined effect sizes. In addition, there was one zero out of the 36 oviposition predation means; this value had units of ‘number of egg rafts laid’ and was replaced with 0.5 rafts. Similar methods for replacing zero values in the treatment mean with small non-zero values have previously been employed <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib134"><span>134</span><span>Thapa et al.</span><span>2017,12,31</span></a></cite>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The final analysis dataset included seven subsets: consumptive effects on survival, non-consumptive effects on survival, oviposition, development (female and male), and size (female and male). The data included 187 observations from 34 studies of consumptive survival, 24 observations from seven studies of non-consumptive survival, 36 observations from 12 studies of oviposition, 14 observations from seven studies of female development, 14 observations from seven studies of male development, 27 observations from 10 studies of female size, and 18 observations from nine studies of male size (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>). These observations covered seven different classes of predator families (<a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2</a>).</p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2" title="Figure 2."> <label data-itemprop="label">Figure 2.</label><img src="index.html.media/fig2.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="mosquito-predator-classes-bold-font-and-families-italicized-font-included-in-the-database-and-the-vector-traits-that-they-may-influence-in-parentheses-predator-images-not-to-scale-and-placed-randomly-with-respect-to-the-different-mosquito-life-stages"> Mosquito predator classes (bold font) and families (italicized font) included in the database and the vector traits that they may influence (in parentheses); predator images not to scale, and placed randomly with respect to the different mosquito life stages.</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Image sources: <a href="https://phylopic.org/" itemscope="" itemtype="http://schema.stenci.la/Link">phylopic.org</a> (CC BY 3.0 or public domain): Actinopterygii (creator: Milton Tan), Arachnida (creators: Sidney Frederic Harmer & Arthur Everett Shipley, vectorized by Maxime Dahirel), Branchiopoda (creator: Africa Gomez), and Insecta (creator: Marie Russell). <a href="https://biorender.com/" itemscope="" itemtype="http://schema.stenci.la/Link">BioRender.com</a>: Amphibia, Hexanauplia, and Malacostraca class silhouettes; mosquito larval instars, pupa, and blood-feeding adult. Trishna Desai: mosquito egg raft.</p> </figcaption> </figure> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-analysis">Data analysis </h3> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>options(stringsAsFactors = F, scipen = 999) library("metafor") library("tidyverse") library("broom") ## Full database: database = read.csv("Predator_effects_on_mosquitoes_database.csv") length(unique(database$Study_ID)) ## Data exclusions & zero substitutions: clean_data = database %>% #Insecticide used with predators filter(Pesticide!="YES") %>% #Multiple prey species filter(Mixed_prey!="YES") %>% #Traits and behavior categories that didn't have enough data for meta-analysis filter(Vec_trait!="OTHER" & Vec_trait!="FECUNDITY" & Vec_trait!="COMPETENCE") %>% filter(!(grepl("BEHAVIOR", Vec_trait) & !grepl("oviposition", Vec_trait))) %>% #Size or development data that were not specific to sex filter(!(Vec_trait=="DEVELOPMENT" & (Sex=="BOTH" | is.na(Sex)))) %>% filter(!(Vec_trait=="SIZE" & (Sex=="BOTH" | is.na(Sex)))) %>% #Pseudo-replication due to uneven representation of reps filter(!(grepl("individual means", Units))) %>% #Immature life stage-specific survival reported after more than ten days filter(!(Pred_effect=="CONSUMPTIVE" & Study_ID=="U106" & grepl("larval", Units))) %>% filter(!(Pred_effect=="CONSUMPTIVE" & Study_ID==1175 & is.na(Sex))) %>% filter(!(Pred_effect=="CONSUMPTIVE" & Study_ID=="U105" & grepl("larvae", Units))) %>% filter(!(Pred_effect=="CONSUMPTIVE" & Study_ID=="U105" & grepl("pupae", Units))) %>% #Inverse dev time filter(!(Vec_trait=="DEVELOPMENT" & grepl("1/median", Units))) %>% #Pseudo-replication due to measuring different body parts of the same mosquitoes filter(!(Study_ID=="1224" & grepl("thorax", Units))) %>% filter(!(Study_ID=="1226" & grepl("thorax", Units))) %>% filter(!(Study_ID=="U166" & grepl("femur", Other_trait))) %>% filter(!(Study_ID=="U166" & grepl("thorax", Other_trait))) %>% #Both the control sd and the predation sd are zeros. This will result in non-positive sampling variances for the ROM measure of effect. filter(!(Con_sd==0 & Pred_sd==0)) %>% #For oviposition observations with a predation treatment mean of zero, replace zero egg rafts with 0.5 egg rafts. mutate(Pred_mean = ifelse(Pred_mean==0 & (grepl("oviposition", Vec_trait)), 0.5, Pred_mean)) %>% #Delete the egg stage survival data if the predation treatment has a mean of zero. These could represent unfertilized egg rafts. filter(!(Vec_trait=="SURVIVAL" & Vec_stage=="EGG" & Pred_mean==0)) %>% #Replace larval survival observations that have zero predator means with 0.5% of the starting larvae. mutate(Pred_mean = ifelse(Pred_mean==0 & (grepl("%", Units) | grepl("percent", Units)), 0.5, ifelse(Pred_mean==0 & (grepl("number", Units) | grepl("#", Units) | grepl("abundance", Units)), (0.005*Num_prey), ifelse(Pred_mean==0 & grepl("proportion", Units), 0.005, Pred_mean)))) %>% #Add the Effect ID column. mutate(Effect_ID = 1: n()) ############################################################################### ######################### CONSUMPTIVE SURVIVAL ############################## ############################################################################### consumptive_survival = clean_data %>% filter(Pred_effect == "CONSUMPTIVE") length(unique(consumptive_survival$Study_ID)) #Calculate the Ratio of Means (ROM) effect size estimate. ROM_consumptive_survival = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = consumptive_survival, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_consumptive_survival <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.uni_consumptive_survival)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_consumptive_survival) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_consumptive_survival, model = "rma", predictor="sei")</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-warnings="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Multilevel mixed effects models: #No moderators ROM.no_mods <- rma.mv(yi = ROM_est, V = ROM_sv, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.no_mods))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Vector genus as a moderator ROM.vec <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Vec_genus-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.vec))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Predator family as a moderator ROM.pred_fam <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.pred_fam))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Predator family and vector genus as moderators ROM.pred_and_vec <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Pred_family+Vec_genus -1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.pred_and_vec))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Predator family and vector genus interacting as moderators ROM.pred_vec_interact <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Pred_family*Vec_genus -1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_consumptive_survival, method = "REML") coef(summary(ROM.pred_vec_interact))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#182 out of 187 consumptive observations, 97%, are of the larval stage. #Testing moderators on observations of larval survival only: larval_stage = consumptive_survival %>% filter(!(is.na(Larval_instar))) length(unique(larval_stage$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_larval_stage = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = larval_stage, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_larval_stage <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_larval_stage, method = "REML") coef(summary(ROM.uni_larval_stage)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_larval_stage) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_larval_stage, model = "rma", predictor="sei") #Multilevel mixed effects models: #No moderators ROM.larval_no_mods <- rma.mv(yi = ROM_est, V = ROM_sv, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_no_mods))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Larval instar as a moderator ROM.larval_instar <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Larval_instar-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_instar))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Vector genus as a moderator ROM.larval_vec <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Vec_genus-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_vec))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Predator family as a moderator ROM.larval_pred <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_pred))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Larval instar and predator family as moderators ROM.larval_instar_and_pred <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Larval_instar+Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_instar_and_pred))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Vector genus and predator family as moderators ROM.larval_pred_and_vec <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Vec_genus+Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_pred_and_vec))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Larval instar and predator family interacting as moderators ROM.larval_instar_pred_interact <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Larval_instar*Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_instar_pred_interact))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Vector genus and predator family interacting as moderators ROM.larval_pred_vec_interact <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Vec_genus*Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_larval_stage, method = "REML") coef(summary(ROM.larval_pred_vec_interact))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Test moderators only on observations that included temperature. #The temperature range is from 16 to 28.25 degrees C. temp = consumptive_survival %>% filter(!(is.na(Temp))) %>% mutate(Temp_squared = Temp*Temp) length(unique(temp$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_temp = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = temp, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_temp <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_temp, method = "REML") coef(summary(ROM.uni_temp))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Assess publication bias visually with a funnel plot. funnel(ROM.uni_temp) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_temp, model = "rma", predictor="sei") #Multilevel mixed effects models: #No moderators ROM.temp_no_mods <- rma.mv(yi = ROM_est, V = ROM_sv, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_temp, method = "REML") coef(summary(ROM.temp_no_mods))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Temp as a moderator ROM.temp <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Temp, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_temp, method = "REML") coef(summary(ROM.temp))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Temp_squared as a moderator ROM.temp_squared <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Temp_squared, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_temp, method = "REML") coef(summary(ROM.temp_squared))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ###################### NON-CONSUMPTIVE SURVIVAL ############################# ############################################################################### non_con_survival = clean_data %>% filter(Pred_effect=="NONCONSUMPTIVE") %>% filter(Vec_trait=="SURVIVAL") length(unique(non_con_survival$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_non_con_survival = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = non_con_survival, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_non_con_survival <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_non_con_survival, method = "REML") coef(summary(ROM.uni_non_con_survival)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_non_con_survival) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_non_con_survival, model = "rma", predictor="sei")</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#There is significant publication bias, so the "trimfill" function is used to impute values from missing studies. trimfill(ROM.uni_non_con_survival, estimator = "L0", side = "left") trimfill(ROM.uni_non_con_survival, estimator = "L0", side = "right") #Visualization of the three imputed values on the left side of the funnel plot: funnel(trimfill(ROM.uni_non_con_survival, estimator = "L0", side = "left"))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ############################## OVIPOSITION ################################## ############################################################################### oviposition = clean_data %>% filter(Vec_trait=="BEHAVIOR: oviposition") length(unique(oviposition$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_oviposition = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = oviposition, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_oviposition <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_oviposition, method = "REML") coef(summary(ROM.uni_oviposition))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Assess publication bias visually with a funnel plot. funnel(ROM.uni_oviposition) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_oviposition, model = "rma", predictor="sei") #Multilevel mixed effects models: #No moderators ROM.oviposition_no_mods <- rma.mv(yi = ROM_est, V = ROM_sv, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_oviposition, method = "REML") coef(summary(ROM.oviposition_no_mods))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Vector genus as a moderator ROM.oviposition_vec <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Vec_genus-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_oviposition, method = "REML") coef(summary(ROM.oviposition_vec))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Predator family as a moderator ROM.oviposition_pred <- rma.mv(yi = ROM_est, V = ROM_sv, mods = ~Pred_family-1, random = list(~1|Effect_ID, ~1|Study_ID/Experimental_ID), data = ROM_oviposition, method = "REML") coef(summary(ROM.oviposition_pred))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ########################## FEMALE DEVELOPMENT ############################### ############################################################################### development_females = clean_data %>% filter(Vec_trait=="DEVELOPMENT") %>% filter(Sex=="FEMALE") length(unique(development_females$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_development_females = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = development_females, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_development_females <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_development_females, method = "REML") coef(summary(ROM.uni_development_females)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_development_females) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_development_females, model = "rma", predictor="sei") #There is significant publication bias, so the "trim-fill" function is used to impute values from missing studies. trimfill(ROM.uni_development_females, estimator = "L0", side = "left") trimfill(ROM.uni_development_females, estimator = "L0", side = "right")</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ########################### MALE DEVELOPMENT ################################ ############################################################################### development_males = clean_data %>% filter(Vec_trait=="DEVELOPMENT") %>% filter(Sex=="MALE") length(unique(development_males$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_development_males = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = development_males, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_development_males <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_development_males, method = "REML") coef(summary(ROM.uni_development_males)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_development_males) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_development_males, model = "rma", predictor="sei") #There is significant publication bias, so the "trimfill" function is used to impute values from missing studies. trimfill(ROM.uni_development_males, estimator = "L0", side = "left") trimfill(ROM.uni_development_males, estimator = "L0", side = "right")</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ############################## FEMALE SIZE ################################## ############################################################################### size_females = clean_data %>% filter(Vec_trait=="SIZE") %>% filter(Sex=="FEMALE") length(unique(size_females$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_size_females = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = size_females, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_size_females <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_size_females, method = "REML") coef(summary(ROM.uni_size_females)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_size_females) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_size_females, model = "rma", predictor="sei") #There is significant publication bias, so the "trimfill" function is used to impute values from missing studies. trimfill(ROM.uni_size_females, estimator = "L0", side = "left") trimfill(ROM.uni_size_females, estimator = "L0", side = "right")</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>#Visualization of the two imputed values on the right side of the funnel plot: funnel(trimfill(ROM.uni_size_females, estimator = "L0", side = "right"))</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-message="FALSE" data-warning="FALSE" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>############################################################################### ############################### MALE SIZE ################################### ############################################################################### size_males = clean_data %>% filter(Vec_trait=="SIZE") %>% filter(Sex=="MALE") length(unique(size_males$Study_ID)) #Calculate the Ratio of Means (ROM). ROM_size_males = escalc(measure = "ROM", n1i = Pred_n, n2i = Con_n, m1i = Pred_mean, m2i = Con_mean, sd1i = Pred_sd, sd2i = Con_sd, data = size_males, var.names = c("ROM_est", "ROM_sv"), append=T) #Random effects model: ROM.uni_size_males <- rma.uni(yi = ROM_est, vi = ROM_sv, data = ROM_size_males, method = "REML") coef(summary(ROM.uni_size_males)) #Assess publication bias visually with a funnel plot. funnel(ROM.uni_size_males) #Use Egger's regression test to quantitatively assess publication bias. regtest(ROM.uni_size_males, model = "rma", predictor="sei")</code></pre> </stencila-code-chunk> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="measuring-effect-sizes-and-heterogeneity">Measuring effect sizes and heterogeneity </h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">All analyses were conducted in R version 4.0.2 <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib108"><span>108</span><span>R Development Core Team</span><span>2019,12,31</span></a></cite>. For each subset of trait data (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>), the ratio of means (ROM) measure of effect size was calculated using the ‘escalc’ function from the ‘metafor’ package; this effect measure is equal to a log-transformed fraction, where predation mean is the numerator and control mean is the denominator <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib143"><span>143</span><span>Viechtbauer</span><span>2009,12,31</span></a></cite>. Random effects models, using the ‘rma.uni’ function, were run with the ROM effect sizes as response variables; each model had a normal error distribution and a restricted maximum likelihood (REML) estimator for τ<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup>, the variance of the distribution of true effect sizes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib143"><span>143</span><span>Viechtbauer</span><span>2009,12,31</span></a></cite>. Although these random effects models could not account for multiple random effects or moderators, they provided overall estimates of the ROM effect sizes and estimates of the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">I</em><sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup> statistics. Each <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">I</em><sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup> statistic represented the percentage of total variation across studies due to heterogeneity <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Higgins et al.</span><span>2002,12,31</span></a></cite>. If the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">I</em><sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup> statistic was equal to or greater than 75%, the heterogeneity was considered to be high <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Higgins et al.</span><span>2002,12,31</span></a></cite>, and high heterogeneity has previously motivated further testing of moderators <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib144"><span>144</span><span>Vincze et al.</span><span>2016,12,31</span></a></cite>.</p> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="assessing-publication-bias"> Assessing publication bias</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Publication bias was assessed by visually inspecting funnel plots and conducting Egger’s regression test (‘regtest’ function) with standard error as the predictor <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib129"><span>129</span><span>Sterne and Egger</span><span>2000,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib143"><span>143</span><span>Viechtbauer</span><span>2009,12,31</span></a></cite></span>. If the Egger’s regression test showed significant evidence of publication bias based on funnel plot asymmetry, the ‘trim and fill’ method (‘trimfill’ function) was used to estimate how the predation effect size might change after imputing values from missing studies <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib37"><span>37</span><span>Duval and Tweedie</span><span>1999,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib38"><span>38</span><span>Duval and Tweedie</span><span>1999,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib143"><span>143</span><span>Viechtbauer</span><span>2009,12,31</span></a></cite></span>. The trim and fill method has previously been recommended for testing the robustness of conclusions related to topics in ecology and evolution <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib64"><span>64</span><span>Jennions and Møller</span><span>2001,12,31</span></a></cite>. Of the two trim and fill estimators, R<sub itemscope="" itemtype="http://schema.stenci.la/Subscript">null</sub> and L<sub itemscope="" itemtype="http://schema.stenci.la/Subscript">null</sub>, that were originally recommended <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib37"><span>37</span><span>Duval and Tweedie</span><span>1999,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib38"><span>38</span><span>Duval and Tweedie</span><span>1999,12,31</span></a></cite></span>, the L<sub itemscope="" itemtype="http://schema.stenci.la/Subscript">null</sub> estimator was used in this study because it is more appropriate for smaller datasets <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib123"><span>123</span><span>Shi and Lin</span><span>2018,12,31</span></a></cite>.</p> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="testing-moderators">Testing moderators</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data subsets that had high heterogeneity, observations from at least 10 studies, and no evidence of publication bias according to Egger’s regression results were analyzed further using multilevel mixed effects models with the ‘rma.mv’ function <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib143"><span>143</span><span>Viechtbauer</span><span>2009,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib61"><span>61</span><span>Higgins et al.</span><span>2019,12,31</span></a></cite></span>. All multilevel mixed effects models had normal error distributions, REML estimators for τ<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup>, and accounted for two random factors: effect size ID, and experiment ID nested within study ID. Moderators, such as predator family, vector genus, larval instar (directly correlated to prey size), and temperature, were tested within each data subset to determine if they affected the observed heterogeneity in ROM effect sizes. For categorical moderators, the intercept of the multilevel mixed effects model was removed, allowing an analysis of variance (ANOVA) referred to as the ‘test of moderators’ to indicate if any of the categories had an effect size different than zero. For data subsets with observations from 10 to 29 studies, only one moderator was tested at a time to account for sample size constraints. For subsets with observations from a higher number of studies (30 or more), up to two moderators were tested at once, and interaction between moderators was also tested. The small sample corrected Akaike Information Criterion (AICc) was used to compare multilevel mixed effects models and to select the model of best fit within each data subset; differences in AICc greater than two were considered meaningful <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib22"><span>22</span><span>Burnham and Anderson</span><span>2003,12,31</span></a></cite>.</p> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="random-effects-models"> Random effects models</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Each data subset (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>) had an <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">I</em><sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">2</span></sup> statistic of greater than 75%, indicating high heterogeneity <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Higgins et al.</span><span>2002,12,31</span></a></cite>. Random effects model results showed that predators consumptively decreased mosquito survival with an effect size of –1.23 (95% CI −1.43,–1.03), p-value < 0.0001, and non-consumptively reduced survival with a smaller effect size of –0.11 (95% CI −0.17,–0.04), p-value = 0.0016. In addition, predators non-consumptively reduced oviposition behavior with an effect size of –0.87 (95% CI −1.31,–0.42), p-value = 0.0001, and mosquito body size was non-consumptively reduced by predators in both males and females; the female effect size was –0.13 (95% CI −0.19,–0.06), p-value = 0.0002, and the male effect size was –0.03 (95% CI −0.06,–0.01), p-value = 0.0184. There was not a significant non-consumptive effect of predators on either male or female development time; the female effect size was –0.01 (95% CI –0.09, 0.07), p-value = 0.7901, and the male effect size was –0.04 (95% CI –0.12, 0.04), p-value = 0.3273.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The Egger’s regression test results showed that the non-consumptive survival subset, both development time subsets (male and female), and the female size subset exhibited funnel plot asymmetry indicative of publication bias. The ‘trim and fill’ procedure identified missing studies in the non-consumptive survival subset and the female size subset, but the procedure did not identify any missing studies in either of the development time subsets. Three studies were estimated to be missing from the non-consumptive survival data, and accounting for imputed values from missing studies resulted in a shift in the predation effect size from –0.11 (95% CI −0.17,–0.04), p-value = 0.0016, to -0.13 (95% CI −0.20,–0.07), p-value < 0.0001. Two studies were estimated to be missing from the female size data, and accounting for imputed values from these missing studies shifted the predation effect size from –0.13 (95% CI −0.19,–0.06), p-value = 0.0002, to -0.10 (95% CI −0.17,–0.03), p-value = 0.0083. Shifts in effect size estimates due to the trim and fill procedure were minor and did not cause any of the observed effects of predators to change direction or become insignificant.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="multilevel-mixed-effects-models">Multilevel mixed effects models</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The consumptive survival and oviposition data subsets met the criteria of high heterogeneity, observations from at least 10 studies, and no evidence of publication bias. Therefore, these data subsets were tested for moderators using multilevel mixed effects models. Predator families that decreased mosquito survival included Cyprinidae: –3.44 (95% CI −5.79,–1.09), p-value = 0.0042; Poeciliidae: –1.42 (95% CI −2.67,–0.16), p-value = 0.0270; Ambystomatidae: –5.18 (95% CI −7.94,–2.42), p-value = 0.0002; Aeshnidae: –2.93 (95% CI −4.80,–1.07), p-value = 0.0020; and Notonectidae: –2.14 (95% CI −3.07,–1.21), p-value < 0.0001 (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3a</a>). Vector genera that experienced significant decreases in survival due to consumptive effects of predators included <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em>: –1.23 (95% CI −1.81,–0.65), p-value < 0.0001; <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em>: –1.34 (95% CI −2.01,–0.66), p-value = 0.0001; and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em>: –1.41 (95% CI −1.96,–0.86), p-value < 0.0001 (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3b</a>). Among all 187 consumptive survival observations from 34 studies, the best model fit, according to AICc value, was achieved when an interaction between predator family and vector genus was included in the model (<a href="#table2" itemscope="" itemtype="http://schema.stenci.la/Link">Table 2</a>). However, among the 163 larval stage consumptive survival observations from 30 studies, adding an interactive term between larval instar (an indicator of prey size) and predator family had a greater improvement on model fit than adding an interactive term between vector genus and predator family (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3c</a>, <a href="#table3" itemscope="" itemtype="http://schema.stenci.la/Link">Table 3</a>). Temperature did not affect the heterogeneity of consumptive survival data, either as a linear moderator: –0.01 (95% CI –0.10, 0.07), p-value = 0.7559, or a quadratic moderator: 0.00 (95% CI 0.00, 0.00), p-value = 0.8184. The best oviposition model fit, according to AICc value, was achieved when vector genus was added as a moderator (<a href="#table4" itemscope="" itemtype="http://schema.stenci.la/Link">Table 4</a>). The mean oviposition effect size was not significantly different than zero for <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em>: 0.32 (95% CI –2.14, 2.79), p-value = 0.7970, or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em>: –0.61 (95% CI –1.83, 0.62), p-value = 0.3329, but for <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> mosquitoes, oviposition was significantly decreased by predator presence: –1.69 (95% CI −2.82,–0.56), p-value = 0.0033 (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4</a>).</p> <table id="table2" itemscope="" itemtype="http://schema.org/Table"> <caption><label data-itemprop="label">Table 2.</label> <div itemprop="caption"> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="candidate-multilevel-mixed-effects-models-of-consumptive-effects-from-predators-on-mosquito-survival-fitted-to-dataset-of-effect-sizes-n--187-from-34-studies-and-ranked-by-corrected-akaikes-information-criterion-aicc"> Candidate multilevel mixed effects models of consumptive effects from predators on mosquito survival, fitted to dataset of effect sizes (n = 187 from 34 studies), and ranked by corrected Akaike’s information criterion (AICc).</h4> </div> </caption> <thead> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Moderator(s)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Test of moderators(degrees of freedom, p-value)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">AICc</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">ΔAICc</th> </tr> </thead> <tbody> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family x vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">28, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">500.5</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.stenci.la/Null">null</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">19, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">507</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">6.5</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family + vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">23, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">508.1</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">7.6</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">5, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">573</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">72.5</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">None</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">----</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">576.5</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">76</span></td> </tr> </tbody> </table> <table id="table3" itemscope="" itemtype="http://schema.org/Table"> <caption><label data-itemprop="label">Table 3.</label> <div itemprop="caption"> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="candidate-multilevel-mixed-effects-models-of-consumptive-effects-from-predators-fitted-to-dataset-of-effect-sizes-where-larval-instar-is-not-missing-n--163-from-30-studies-and-ranked-by-corrected-akaikes-information-criterion-aicc"> Candidate multilevel mixed effects models of consumptive effects from predators, fitted to dataset of effect sizes where larval instar is not missing (n = 163 from 30 studies), and ranked by corrected Akaike’s information criterion (AICc).</h4> </div> </caption> <thead> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Moderator(s)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Test of moderators(degrees of freedom, p-value)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">AICc</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">ΔAICc</th> </tr> </thead> <tbody> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family x larval instar</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">25, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">429.2</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.stenci.la/Null">null</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family + larval instar</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">19, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">443.5</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">14.3</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family x vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">25, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">455</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">25.8</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">17, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">456.8</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">27.6</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family + vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">21, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">458.4</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">29.2</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Larval instar</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">3, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">503.1</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">73.9</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">5, < 0.0001</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">504.7</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">75.5</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">None</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">----</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">508.5</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">79.3</span></td> </tr> </tbody> </table> <table id="table4" itemscope="" itemtype="http://schema.org/Table"> <caption><label data-itemprop="label">Table 4.</label> <div itemprop="caption"> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="candidate-multilevel-mixed-effects-models-of-non-consumptive-effects-of-predators-on-mosquito-oviposition-behavior-fitted-to-dataset-of-effect-sizes-n--36-from-12-studies-and-ranked-by-corrected-akaikes-information-criterion-aicc"> Candidate multilevel mixed effects models of non-consumptive effects of predators on mosquito oviposition behavior, fitted to dataset of effect sizes (n = 36 from 12 studies), and ranked by corrected Akaike’s information criterion (AICc).</h4> </div> </caption> <thead> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Moderator(s)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Test of moderators(degrees of freedom, p-value)</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">AICc</th> <th itemscope="" itemtype="http://schema.stenci.la/TableCell">ΔAICc</th> </tr> </thead> <tbody> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vector genus</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">3, 0.0149</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">122.1</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.stenci.la/Null">null</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">None</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">----</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">125.2</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">3.1</span></td> </tr> <tr itemscope="" itemtype="http://schema.stenci.la/TableRow"> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Predator family</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell">12, 0.8855</td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">167.9</span></td> <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span data-itemtype="http://schema.org/Number">45.8</span></td> </tr> </tbody> </table> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3" title="Figure 3."> <label data-itemprop="label">Figure 3.</label><img src="index.html.media/fig3.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="effect-sizes-and-95--confidence-intervals-for-consumptive-effects-of-predators-for-different-categories-of-moderators-with-number-of-studies-in-parentheses"> Effect sizes and 95 % confidence intervals for consumptive effects of predators, for different categories of moderators (with number of studies in parentheses).</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">a</strong>) predator family with predator class in the right-hand column, (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">b</strong>) vector genus, and (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">c</strong>) larval instar. </p> </figcaption> </figure> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4" title="Figure 4."> <label data-itemprop="label">Figure 4.</label><img src="index.html.media/fig4.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="oviposition-effect-sizes-and-95--confidence-intervals-for-different-categories-of-vector-genus-with-number-of-studies-in-parentheses"> Oviposition effect sizes and 95 % confidence intervals for different categories of vector genus (with number of studies in parentheses).</h4> </figcaption> </figure> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In this study, laboratory and semi-field empirical data were obtained through a systematic literature review and used to conduct a meta-analysis that assessed consumptive and non-consumptive effects of predators on mosquito prey. Some results agree with previously observed trends, such as greater consumptive effects from larger predators <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib74"><span>74</span><span>Kumar et al.</span><span>2007,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Peters</span><span>1982,12,31</span></a></cite></span> and no oviposition response to predator cues among container-breeding <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> mosquitoes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>. However, this meta-analysis revealed additional trends. Mosquito larval instar had an important role in moderating consumptive effects of predators, likely because of its direct correlation to prey size. Furthermore, a small, but significant, decrease in mosquito survival due to non-consumptive effects of predators was observed, suggesting that mosquitoes can be ‘scared to death’ by predators <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib106"><span>106</span><span>Preisser et al.</span><span>2004,12,31</span></a></cite>. Both male and female body sizes were also reduced among mosquitoes that had been exposed to predators, and predator avoidance during oviposition was observed among female <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> mosquitoes. Effects of predators on different vector traits, particularly survival, body size, and oviposition behavior, have the potential to influence infectious disease dynamics.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="consumptive-effects-of-predators-on-survival">Consumptive effects of predators on survival</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Several larger predators reduced mosquito survival, including freshwater fish (Cyprinidae and Poeciliidae), salamander larvae (Ambystomatidae), dragonfly larvae (Aeshnidae), and backswimmers (Notonectidae) (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3a</a>). This finding is consistent with a previous analysis which showed a positive linear relationship between predator body mass and ingestion rate across taxa <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Peters</span><span>1982,12,31</span></a></cite>. In addition, more effect size heterogeneity in the consumptive survival data was explained by an interaction between predator family and larval instar than was explained by an interaction between predator family and vector genus (<a href="#table3" itemscope="" itemtype="http://schema.stenci.la/Link">Table 3</a>). This result suggests that the relative sizes of predator and prey groups could play a more important role in determining consumptive mosquito survival than variations in predator responses to different behaviors of prey genera, which are likely to be shaped by the degree of shared evolutionary history between trophic levels <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Buchanan et al.</span><span>2016,12,31</span></a></cite>. Larval instar is an indicator of mosquito size, and previous modeling work has provided evidence of prey size selection by predators to maximize energetic gain <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib92"><span>92</span><span>Mittelbach</span><span>1980,12,31</span></a></cite>. While smaller cyclopoid copepods are more effective against early instar mosquito larvae <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib34"><span>34</span><span>Dieng et al.</span><span>2001,12,31</span></a></cite>, larger predators including tadpoles, giant water bugs, dragonfly larvae, fish, and backswimmers are more effective against late instar larvae <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib75"><span>75</span><span>Kweka et al.</span><span>2010,12,31</span></a></cite>.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="non-consumptive-effects-of-predators-on-survival">Non-consumptive effects of predators on survival</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Exposure to predation cues significantly lowered mosquito survival, and this non-consumptive effect has also been observed in dragonfly larvae prey (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Leucorrhinia intacta</em>) that were exposed to caged predators <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib90"><span>90</span><span>McCauley et al.</span><span>2010,12,31</span></a></cite>. The reduction in mosquito survival from non-consumptive effects of predators was significantly smaller than the reduction that was observed from consumptive effects. This is partially due to the practical constraints of most experimental designs, which cause consumptive and non-consumptive effects of predators on survival to be grouped together and reported as consumptive effects. The greater impact of combined consumptive and non-consumptive effects, in comparison to only non-consumptive effects, has previously been observed in pea aphids (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Acyrthosiphon pisum</em>) <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib96"><span>96</span><span>Nelson et al.</span><span>2003,12,31</span></a></cite>.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="non-consumptive-effects-of-predators-on-body-size">Non-consumptive effects of predators on body size</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While predators did not significantly impact mosquito development time through non-consumptive effects in either sex, mosquito body size was decreased by the non-consumptive effects of predators in both sexes. Smaller body size is associated with lower reproductive success in mosquitoes because smaller females lay fewer eggs <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib16"><span>16</span><span>Blackmore and Lord</span><span>1999,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib84"><span>84</span><span>Lyimo and Takken</span><span>1992,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib98"><span>98</span><span>Oliver and Howard</span><span>2010,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib130"><span>130</span><span>Styer et al.</span><span>2006,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib138"><span>138</span><span>Tsunoda et al.</span><span>2009,12,31</span></a></cite></span>, and smaller males produce less sperm <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib55"><span>55</span><span>Hatala et al.</span><span>2017,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib105"><span>105</span><span>Ponlawat and Harrington</span><span>2006,12,31</span></a></cite></span>. These effects suggest that predation could non-consumptively reduce mosquito population growth. The smaller size of mosquitoes exposed to predators could also limit disease transmission. Vector lifespan contributes disproportionately to disease transmission because older vectors are more likely to have been exposed to pathogens, more likely to already be infectious after having survived the extrinsic incubation period, and more likely to survive long enough to bite subsequent hosts <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Cator et al.</span><span>2019,12,31</span></a></cite>. It is well-established that smaller mosquito body size is associated with shorter mosquito lifespan <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Araújo et al.</span><span>2011,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib56"><span>56</span><span>Hawley</span><span>1984,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib111"><span>111</span><span>Reisen et al.</span><span>1983,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib112"><span>112</span><span>Reiskind and Lounibos</span><span>2008,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib149"><span>149</span><span>Xue et al.</span><span>2009,12,31</span></a></cite></span>. Therefore, non-consumptive effects of predators may limit the transmission of mosquito-borne diseases.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="non-consumptive-effects-of-predators-on-oviposition-behavior">Non-consumptive effects of predators on oviposition behavior</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Predator presence also non-consumptively reduced oviposition behavior in adult female mosquitoes. Meta-regression results showed that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> females significantly avoid oviposition sites that contain predators or predator cues, but <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em> females do not avoid these sites, despite a slight non-significant trend toward predator avoidance in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em> (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4</a>). Both <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em> mosquitoes have an ‘all-or-none’ oviposition strategy <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib66"><span>66</span><span>Johnson and Fonseca</span><span>2013,12,31</span></a></cite>, in which they lay hundreds of rapidly hatching eggs in rafts on the water’s surface <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Day</span><span>2015,12,31</span></a></cite>. Such an oviposition strategy is conducive to evolving predator avoidance behaviors, and a previous meta-analysis showed significant predator avoidance in both <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em> during oviposition <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>. Conversely, it is likely that an oviposition response to predation is not particularly advantageous for <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> because the delayed hatching of their eggs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Day</span><span>2015,12,31</span></a></cite> can prevent the level of predation risk at the time of oviposition from matching the level of predation risk present in the eventual larval environment <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>. The predator avoidance response in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> species that lay their eggs above the water’s edge in containers has previously been described as ‘non-existent’ <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>. Both <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> species included in this study’s oviposition data subset, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Ae. albopictus</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Ae. aegypti</em>, meet the criterion of ovipositing above water in containers <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib67"><span>67</span><span>Juliano</span><span>2008,12,31</span></a></cite>. Predator avoidance during oviposition has previously been found to increase the mosquito population size at equilibrium <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib127"><span>127</span><span>Spencer et al.</span><span>2001,12,31</span></a></cite>. However, this study’s results and those of a previous meta-analysis <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite> suggest that models of oviposition site selection, such as those using parameters from Notonectidae predators and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culiseta</em> prey <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib70"><span>70</span><span>Kershenbaum et al.</span><span>2011,12,31</span></a></cite>, are not generalizable to <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> vectors.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="implications-for-west-nile-virus-disease-dynamics">Implications for West Nile Virus disease dynamics</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Predator avoidance during oviposition by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> mosquitoes (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4</a>) may be of particular importance to West Nile virus (WNV) disease dynamics. Previous work has shown that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. restuans</em>, and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. tarsalis</em> all avoid predator habitats <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib146"><span>146</span><span>Vonesh and Blaustein</span><span>2009,12,31</span></a></cite>, and that <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> is the primary bridge vector of WNV responsible for spill-over transmission from avian reservoir hosts to humans <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib44"><span>44</span><span>Fonseca et al.</span><span>2003,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib51"><span>51</span><span>Hamer et al.</span><span>2007,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib72"><span>72</span><span>Kramer et al.</span><span>2007,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib8"><span>8</span><span>Andreadis</span><span>2011,12,31</span></a></cite></span>. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> mosquitoes can live in permanent aquatic environments, such as ground pools <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3"><span>3</span><span>Amini et al.</span><span>2019,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib11"><span>11</span><span>Barr</span><span>1966,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib33"><span>33</span><span>Dida et al.</span><span>2017,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib133"><span>133</span><span>Sulesco et al.</span><span>2014,12,31</span></a></cite></span>, ponds <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib83"><span>83</span><span>Lühken et al.</span><span>2014,12,31</span></a></cite>, stream edges <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3"><span>3</span><span>Amini et al.</span><span>2019,12,31</span></a></cite>, and lake edges <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib145"><span>145</span><span>Vinogradova</span><span>1999,12,31</span></a></cite> that are more common in rural areas, but <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> are also found in urban and suburban residential areas, where they typically breed in artificial containers <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib133"><span>133</span><span>Sulesco et al.</span><span>2014,12,31</span></a></cite>, including tires <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib83"><span>83</span><span>Lühken et al.</span><span>2014,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib97"><span>97</span><span>Nikookar et al.</span><span>2016,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib140"><span>140</span><span>Verna</span><span>2014,12,31</span></a></cite></span>, rainwater tanks <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib136"><span>136</span><span>Townroe and Callaghan</span><span>2013,12,31</span></a></cite>, and catch basins <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib45"><span>45</span><span>Gardner et al.</span><span>2011,12,31</span></a></cite>. Small artificial containers, such as discarded tires, are generally unlikely to harbor larger predators, including freshwater fish (Cyprinidae and Poeciliidae), salamander larvae (Ambystomatidae), dragonfly larvae (Aeshnidae), and backswimmers (Notonectidae), because temporary aquatic environments cannot support the relatively long development times of these organisms. The mean dispersal distance of adult <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> mosquitoes is greater than one kilometer <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib27"><span>27</span><span>Ciota et al.</span><span>2011,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Hamer et al.</span><span>2013,12,31</span></a></cite></span>, and female <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> have exhibited longer dispersal distances after developing in the presence of a fish predator <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Alcalay et al.</span><span>2017,12,31</span></a></cite>. Therefore, predator avoidance during oviposition may cause <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> populations to disperse from permanent aquatic environments in more rural areas to artificial container environments in urbanized areas, where the risk of human WNV infection is higher <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Brown et al.</span><span>2007,12,31</span></a></cite>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Predator cue levels may be altered by climate conditions, and these changes in cue levels can impact WNV transmission to humans. Drought has previously been associated with human WNV cases <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib65"><span>65</span><span>Johnson and Sukhdeo</span><span>2012,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib86"><span>86</span><span>Marcantonio et al.</span><span>2014,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib115"><span>115</span><span>Roehr</span><span>2011,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib122"><span>122</span><span>Shaman et al.</span><span>2004,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib42"><span>42</span><span>Epstein and Defilippo</span><span>2000,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib102"><span>102</span><span>Paull et al.</span><span>2016,12,31</span></a></cite></span>, but the association has thus far lacked a clear underlying mechanism. Under drought conditions, the density of aquatic organisms increases and predation pressures can intensify due to compressed space and high encounter rates <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Amundrud et al.</span><span>2018,12,31</span></a></cite>. A previous study of a stream ecosystem found that impacts of fish predation are more severe during the dry season <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib36"><span>36</span><span>Dudgeon</span><span>1992,12,31</span></a></cite>. In addition, reductions in water volume can facilitate consumption of mosquito larvae by crane fly larvae (Tipulidae), whereas mosquito consumption by tipulids was not observed at a higher water level <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Amundrud et al.</span><span>2018,12,31</span></a></cite>. Laboratory and semi-field studies have shown that mosquitoes respond to a gradient of predator cues <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib118"><span>118</span><span>Roux et al.</span><span>2013,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib125"><span>125</span><span>Silberbush and Blaustein</span><span>2010,12,31</span></a></cite></span>. The frequency of larval anti-predator behavior is correlated with the concentration of predator cues <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib118"><span>118</span><span>Roux et al.</span><span>2013,12,31</span></a></cite>, and adult female mosquitoes prefer oviposition sites with lower predator densities <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib125"><span>125</span><span>Silberbush and Blaustein</span><span>2010,12,31</span></a></cite>. Therefore, as predator cue levels increase due to drought, permanent aquatic habitats are likely to transition from suitable oviposition sites for one generation of female mosquitoes, to unsuitable oviposition sites for the next generation.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">When suitable oviposition sites are absent, females retain their eggs until sites become available <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Bentley and Day</span><span>1988,12,31</span></a></cite>. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> females can retain their eggs for up to five weeks, allowing them enough time to find container sites with low predation risk, often located in residential areas <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib66"><span>66</span><span>Johnson and Fonseca</span><span>2013,12,31</span></a></cite>. The movement of gravid female <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> to residential areas increases the risk of WNV spill-over to humans because these vectors are likely to have already blood-fed at least once <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib28"><span>28</span><span>Clements</span><span>1991,12,31</span></a></cite>, suggesting that they have a higher risk of WNV infection, relative to non-gravid mosquitoes. This is consistent with studies that have reported associations between drought and WNV-infected mosquitoes in urban and residential areas <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib65"><span>65</span><span>Johnson and Sukhdeo</span><span>2012,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib102"><span>102</span><span>Paull et al.</span><span>2016,12,31</span></a></cite></span>. In addition, vertical transmission of WNV from gravid females to their progeny may occur during oviposition <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib117"><span>117</span><span>Rosen</span><span>1987,12,31</span></a></cite>, when the virus is transmitted by an accessory gland fluid that attaches eggs to one another <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib95"><span>95</span><span>Nelms et al.</span><span>2012,12,31</span></a></cite>. Because the rate of vertical transmission in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> increases with the number of days following WNV infection <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Anderson et al.</span><span>2007,12,31</span></a></cite>, extended searches for oviposition sites due to drought could increase the frequency of vertical transmission. However, the impact of vertical transmission on WNV epidemics is thought to be minimal because when transmission to an egg raft did occur, only 4.7% of the progeny were found to be infected as adults <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Anderson et al.</span><span>2007,12,31</span></a></cite>, and only about half of those infected adults are estimated to be female. In summary, the movement of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cx. pipiens</em> females toward more residential areas, combined with potential limited WNV amplification from increased vertical transmission, suggests that the vector trait of predator avoidance during oviposition can serve as a plausible explanation for associations between drought and human WNV cases.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Another theory for the association between drought and human WNV cases is based on the hypothesis that increased contact between mosquito vectors and passerine reservoir hosts occurs during drought conditions <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib102"><span>102</span><span>Paull et al.</span><span>2016,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib122"><span>122</span><span>Shaman et al.</span><span>2004,12,31</span></a></cite></span>. The proposed aggregation of bird and mosquito populations during drought was originally thought to occur in humid, densely vegetated hammocks – a type of habitat that is specific to southern Florida <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib122"><span>122</span><span>Shaman et al.</span><span>2004,12,31</span></a></cite>, but WNV incidence is more consistently clustered in other regions of the US, particularly the Northern Great Plains <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>CENTERS FOR DISEASE CONTROL AND PREVENTION</span><span>2020,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib132"><span>132</span><span>Sugumaran et al.</span><span>2008,12,31</span></a></cite></span>. Northern cardinals (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Cardinalis cardinalis</em>), American robins (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Turdus migratorius</em>), and house sparrows (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Passer domesticus</em>) were among the bird species that most frequently tested seropositive for WNV antibodies in 2005 and 2006 in Chicago, where high numbers of human cases were reported <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib52"><span>52</span><span>Hamer et al.</span><span>2007,12,31</span></a></cite>, and these passerine species are more abundant in residential areas, regardless of precipitation patterns <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib6"><span>6</span><span>Anderson</span><span>2005,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>Beddall</span><span>1962,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib79"><span>79</span><span>Lepczyk et al.</span><span>2007,12,31</span></a></cite></span>. Apart from drought, landowners’ participation in supplemental bird feeding, providing bird houses, gardening, and maintaining vegetation can strongly influence passerine abundance in residential areas <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib78"><span>78</span><span>Lepczyk et al.</span><span>2003,12,31</span></a></cite>. Furthermore, as terrestrial foragers that can obtain hydration from their diet of insects, fruits, and other plant material <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib5"><span>5</span><span>Anderson</span><span>2005,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib20"><span>20</span><span>Brzek et al.</span><span>2008,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Malmborg and Willson</span><span>1987,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib113"><span>113</span><span>Renne et al.</span><span>1999,12,31</span></a></cite></span>, passerine reservoir hosts of WNV are less likely to move in response to drought than the mosquito vectors of WNV, which have obligate aquatic life stages.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While hatch-year birds are more vulnerable to mosquito biting, and thus contribute to the amplification of WNV <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib52"><span>52</span><span>Hamer et al.</span><span>2007,12,31</span></a></cite>, it is illogical to expect an increased abundance of hatch-year birds during drought conditions. However, some have argued that in cases where drought decreases the abundance of juvenile birds, the ratio of mosquitoes to birds increases, and this could lead to higher WNV prevalence in the mosquito population <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib102"><span>102</span><span>Paull et al.</span><span>2016,12,31</span></a></cite>. Although reductions in both hatching success <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib46"><span>46</span><span>George et al.</span><span>1991,12,31</span></a></cite> and survival of recently fledged birds <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib150"><span>150</span><span>Yackel Adams et al.</span><span>2005,12,31</span></a></cite> have been observed during drought conditions, the impact of drought on avian abundance varies widely by species <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib141"><span>141</span><span>Verner and Purcell</span><span>1998,12,31</span></a></cite>. In particular, synanthropic species, such as those likely to harbor WNV, are less negatively affected by drought <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Albright et al.</span><span>2008,12,31</span></a></cite>. Additionally, the droughts that impact avian abundance often occur over much longer periods of time than the seasonal droughts that predict WNV transmission to humans. For example, avian abundance has been modeled based on precipitation metrics spanning 32 weeks, and house wren (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Troglodytes aedon</em>) abundance has been predicted by precipitation averages spanning four years <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib141"><span>141</span><span>Verner and Purcell</span><span>1998,12,31</span></a></cite>. Finally, birds with higher levels of stress hormones are more likely to be fed on by mosquitoes, and certain factors associated with residential areas, such as road noise, light pollution, and pesticide exposure, can cause avian stress <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib47"><span>47</span><span>Gervasi et al.</span><span>2015,12,31</span></a></cite>. Therefore, elevated avian stress hormones in these habitats may contribute to WNV prevalence in the mosquito population, independent of drought conditions.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="implications-for-mosquito-borne-disease-modeling">Implications for mosquito-borne disease modeling</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Although the aquatic phase of the mosquito life cycle is often overlooked in mathematical models of mosquito-borne pathogen transmission <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib110"><span>110</span><span>Reiner et al.</span><span>2012,12,31</span></a></cite>, vector survival at immature stages plays an important role in determining mosquito population abundance, which is an essential factor for predicting disease transmission <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Beck-Johnson et al.</span><span>2012,12,31</span></a></cite>. The results of this study show that mosquito survival decreases among the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em>, and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> genera due to consumptive effects of predators (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3b</a>), and that there is also a reduction in mosquito survival due to non-consumptive effects. Other studies have demonstrated that aquatic predators dramatically impact mosquito survival and abundance. For example, a biocontrol intervention relying on the application of copepod predators eliminated <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes albopictus</em> from three communes in Nam Dinh, Vietnam, where dengue transmission was previously detected, and reduced vector abundance by 86–98% in three other communes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib69"><span>69</span><span>Kay et al.</span><span>2001,12,31</span></a></cite>. Conversely, the annual abundance of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Anopheles</em> mosquitoes was observed to increase 15-fold in semi-permanent wetlands in the year following a drought, likely because the drought eliminated aquatic predators from wetlands that dried completely, and mosquitoes were able to re-colonize newly formed aquatic habitats more quickly than their most effective predators <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib26"><span>26</span><span>Chase and Knight</span><span>2002,12,31</span></a></cite>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While relationships between temperature and different vector traits, such as fecundity and lifespan, have been incorporated into models of temperature effects on mosquito population density <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>El Moustaid and Johnson</span><span>2018,12,31</span></a></cite>, models of predator effects on vector borne disease transmission have focused primarily on the impacts of predation on vector survival. Previous models have shown that predators of vector species can decrease or eliminate pathogen infection in host populations as vector fecundity increases <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib94"><span>94</span><span>Moore et al.</span><span>2009,12,31</span></a></cite>. The findings of this meta-analysis suggest that predators also decrease vector fecundity through non-consumptive effects on vector body size. In addition, the entomological inoculation rate (EIR) is likely to be reduced by effects of predators on mosquito fecundity and lifespan, as well as effects of predators on mosquito survival. The EIR has been defined as the product of three variables: (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">m</em>) the number of mosquitoes per host, (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">a</em>) the daily rate of mosquito biting, and (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">s</em>) the proportion of mosquitoes that are infectious <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Beck-Johnson et al.</span><span>2012,12,31</span></a></cite>. Based on this study’s findings, predators are likely to decrease the number of mosquitoes per host by reducing mosquito survival through both consumptive and non-consumptive effects, and by reducing mosquito fecundity through non-consumptive effects on body size. In addition, predators are likely to decrease the proportion of mosquitoes that are infectious by shortening the vector lifespan through non-consumptive effects on body size. The relationship between mosquito body size and biting rate is unclear, with some studies showing higher biting rates among larger mosquitoes <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Araújo et al.</span><span>2011,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib50"><span>50</span><span>Gunathilaka et al.</span><span>2018,12,31</span></a></cite></span>, and others reporting higher biting rates among smaller mosquitoes <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib43"><span>43</span><span>Farjana and Tuno</span><span>2012,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Leisnham et al.</span><span>2007,12,31</span></a></cite></span>. The links between factors that influence the EIR and observed effects of predators on mosquito prey demonstrate the necessity of including both consumptive and non-consumptive effects of predators in models of mosquito-borne disease.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="conclusion">Conclusion</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This meta-analysis on mosquito predation demonstrates that predators not only play an important role in directly reducing mosquito populations, but also have non-consumptive effects on surviving mosquitoes that may ultimately reduce further population growth and decrease disease transmission. While families of larger sized predators were effective in reducing mosquito survival, other factors, such as impacts on native species, as well as the economic cost of mass-rearing and field applications <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib73"><span>73</span><span>Kumar and Hwang</span><span>2005,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib107"><span>107</span><span>Pyke</span><span>2007,12,31</span></a></cite></span>, should be carefully considered before selecting a predator as a suitable biocontrol agent. Predictive disease models are likely to be more reliable when the non-consumptive effects of predation are incorporated. Although exposure of mosquito larvae to predators is commonplace in outdoor field settings, it remains rare in most laboratory-based assessments of vector traits. Therefore, mosquitoes observed in nature are likely to have smaller body sizes than those observed under optimal laboratory conditions. It is important for disease modelers to recognize these impacts of predation on vector traits as they can reduce mosquito population growth and limit disease transmission due to shorter vector lifespans. Within the WNV disease system, consideration of the oviposition behavioral response to predation cues by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Culex</em> vectors can improve current understanding of the association between drought and human cases. This study provides general estimates of the effects of predators on selected mosquito traits for use in predictive disease models.</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="future-directions">Future directions</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Modeling efforts that aim to optimize the application of biocontrol predators should also consider incorporating predator effects on vector survival, fecundity, and lifespan. These additions to predictive models of various biocontrol interventions are likely to help public health officials choose the most cost-effective strategies for limiting disease transmission. In the 60-study database that was compiled, only one study was designed to directly measure the effect of larval-stage predation on vector competence <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib119"><span>119</span><span>Roux et al.</span><span>2014,12,31</span></a></cite>. Therefore, future efforts to assess the impact of predators on mosquito-borne disease transmission should prioritize experimental studies in which infected mosquito larvae are observed throughout an initial period of aquatic exposure to predators, followed by a period of blood-feeding in the adult stage.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Two studies from the compiled database examined the compatibility of predators with <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Bacillus thuringiensis</em> var. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">israelensis</em> (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Bti</em>), a commonly used bacterial biocontrol agent <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib25"><span>25</span><span>Chansang et al.</span><span>2003,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib99"><span>99</span><span>Beeck et al.</span><span>2015,12,31</span></a></cite></span>. Previous studies have supported the simultaneous application of cyclopoid copepod predators and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Bti</em><span data-itemtype="http://schema.stenci.la/Null">null</span><span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib88"><span>88</span><span>Marten et al.</span><span>1992,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib135"><span>135</span><span>Tietze et al.</span><span>1993,12,31</span></a></cite></span>, but additional analyses are needed on the use of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Bti</em> with other families of mosquito predators. Populations of other insect pests, such as the southern green stink bug (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nezara viridula</em>), are known to be regulated by both predators and parasites <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Ehler</span><span>2001,12,31</span></a></cite>. The literature search conducted for this meta-analysis returned studies on water mite parasites <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib109"><span>109</span><span>Rajendran and Prasad</span><span>1993,12,31</span></a></cite> and nematode parasitoids <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Valdez</span><span>2005,12,31</span></a></cite> of mosquitoes, and ascogregarine parasites have previously been evaluated as biocontrol agents against <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Aedes</em> mosquitoes <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib137"><span>137</span><span>Tseng</span><span>2006,12,31</span></a></cite>. A more thorough review of the impacts of parasites and parasitoids on vector traits, such as survival, fecundity, and lifespan, is needed before incorporating these potential biocontrol agents into integrated vector control plans.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Three studies in the 60-study database included experiments where two mosquito prey species were made available to the predator species <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib48"><span>48</span><span>Grill and Juliano</span><span>1995,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib49"><span>49</span><span>Griswold and Lounibos</span><span>2004,12,31</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib91"><span>91</span><span>Micieli et al.</span><span>2001,12,31</span></a></cite></span>. In these cases, the effect size measurement for each mosquito species could be influenced by interspecific competition, or a preference of the predator species for a certain prey species. Hetero-specific prey observations were excluded from this meta-analysis, but future analyses centered on the concepts of interspecific competition or predator preferences might further evaluate these data. In addition, this meta-analysis investigated consumptive and non-consumptive effects of predators separately. 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